



"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case   					=				logging.get_logger(__name__)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=False	)						->				Optional[int]:
  _snake_case   	=      []
  for i in range(config.num_hidden_layers	):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""")	)
    rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")	)
    rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")	)
    rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")	)
    rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")	)
    rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")	)
    rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")	)
    rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")	)
    rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")	)
    rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")	)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('''cls_token''', '''deit.embeddings.cls_token'''),
          ('''dist_token''', '''deit.embeddings.distillation_token'''),
          ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
          ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
          ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
      ]	)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('''norm.weight''', '''layernorm.weight'''),
            ('''norm.bias''', '''layernorm.bias'''),
            ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
            ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
        ]	)

    # if just the base model, we should remove "deit" from all keys that start with "deit"
    _snake_case   	=      [(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''	) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ('''norm.weight''', '''deit.layernorm.weight'''),
            ('''norm.bias''', '''deit.layernorm.bias'''),
            ('''head.weight''', '''cls_classifier.weight'''),
            ('''head.bias''', '''cls_classifier.bias'''),
            ('''head_dist.weight''', '''distillation_classifier.weight'''),
            ('''head_dist.bias''', '''distillation_classifier.bias'''),
        ]	)

  return rename_keys




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=False	)						->				Tuple:
  for i in range(config.num_hidden_layers	):
    if base_model:
      _snake_case   	=      ''''''
    else:
      _snake_case   	=      '''deit.'''
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case   	=      state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""	)
    _snake_case   	=      state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""	)
    # next, add query, keys and values (in that order) to the state dict
    _snake_case   	=      in_proj_weight[
        : config.hidden_size, :
    ]
    _snake_case   	=      in_proj_bias[: config.hidden_size]
    _snake_case   	=      in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case   	=      in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case   	=      in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case   	=      in_proj_bias[-config.hidden_size :]




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Optional[Any]:
  _snake_case   	=      dct.pop(lowerCAmelCase_	)
  _snake_case   	=      val




def 			snake_case		(		)						->				List[Any]:
  _snake_case   	=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case   	=      Image.open(requests.get(lowerCAmelCase_  ,						stream=lowerCAmelCase_	).raw	)
  return im




@torch.no_grad()
def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Dict:
  _snake_case   	=      DeiTConfig()
  # all deit models have fine-tuned heads
  _snake_case   	=      False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  _snake_case   	=      1000
  _snake_case   	=      '''huggingface/label-files'''
  _snake_case   	=      '''imagenet-1k-id2label.json'''
  _snake_case   	=      json.load(open(hf_hub_download(lowerCAmelCase_  ,						lowerCAmelCase_  ,						repo_type='''dataset'''	)  ,						'''r'''	)	)
  _snake_case   	=      {int(lowerCAmelCase_	): v for k, v in idalabel.items()}
  _snake_case   	=      idalabel
  _snake_case   	=      {v: k for k, v in idalabel.items()}
  _snake_case   	=      int(deit_name[-6:-4]	)
  _snake_case   	=      int(deit_name[-3:]	)
  # size of the architecture
  if deit_name[9:].startswith('''tiny'''	):
    _snake_case   	=      192
    _snake_case   	=      768
    _snake_case   	=      12
    _snake_case   	=      3
  elif deit_name[9:].startswith('''small'''	):
    _snake_case   	=      384
    _snake_case   	=      1536
    _snake_case   	=      12
    _snake_case   	=      6
  if deit_name[9:].startswith('''base'''	):
    pass
  elif deit_name[4:].startswith('''large'''	):
    _snake_case   	=      1024
    _snake_case   	=      4096
    _snake_case   	=      24
    _snake_case   	=      16

  # load original model from timm
  _snake_case   	=      timm.create_model(lowerCAmelCase_  ,						pretrained=lowerCAmelCase_	)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  _snake_case   	=      timm_model.state_dict()
  _snake_case   	=      create_rename_keys(lowerCAmelCase_  ,						lowerCAmelCase_	)
  for src, dest in rename_keys:
    rename_key(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
  read_in_q_k_v(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)

  # load HuggingFace model
  _snake_case   	=      DeiTForImageClassificationWithTeacher(lowerCAmelCase_	).eval()
  model.load_state_dict(lowerCAmelCase_	)

  # Check outputs on an image, prepared by DeiTImageProcessor
  _snake_case   	=      int(
      (256 / 224) * config.image_size	)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  _snake_case   	=      DeiTImageProcessor(size=lowerCAmelCase_  ,						crop_size=config.image_size	)
  _snake_case   	=      image_processor(images=prepare_img()  ,						return_tensors='''pt'''	)
  _snake_case   	=      encoding['''pixel_values''']
  _snake_case   	=      model(lowerCAmelCase_	)

  _snake_case   	=      timm_model(lowerCAmelCase_	)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(lowerCAmelCase_  ,						outputs.logits  ,						atol=1E-3	)

  Path(lowerCAmelCase_	).mkdir(exist_ok=lowerCAmelCase_	)
  print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""	)
  model.save_pretrained(lowerCAmelCase_	)
  print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
  image_processor.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
     snake_case   					=				argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--deit_name''',
         default='''vit_deit_base_distilled_patch16_224''',
         type=str,
         help='''Name of the DeiT timm model you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )

     snake_case   					=				parser.parse_args()
     convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)












'''simple docstring'''






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer






def   __A    (				a_    :	str   ,a_    :	str   ,**a_    :	int						):
		lowerCAmelCase  :     str       						= AutoConfig.from_pretrained(a_   ,**a_						)
		lowerCAmelCase  :     Any       						= AutoModelForSeqaSeqLM.from_config(a_						)
		model.save_pretrained(a_						)
		AutoTokenizer.from_pretrained(a_						).save_pretrained(a_						)
		return model


if __name__ == "__main__":
					fire.Fire(save_randomly_initialized_version)



